Given a user data query to an information-seeking system, there may be multiple ways for the system to respond to such a query. Ideally, the response should be tailored to the user interaction context, including the query expression, the retrieval result, and user interests.
Since it is difficult to predict how a course of user interaction would unfold, it is impractical to plan all possible responses, including their content and form, in advance. Thus, researchers and practitioners have experimented with the concept of automating the generation of system responses. One key step in such an automation process is data content determination, a process that dynamically chooses data content in response to user queries.
Existing approaches use a rule-based or schema-based approach to determine response content or select content by specific factors, such as content importance, user knowledge, user preferences, or user tasks. However in reality, a wide variety of factors, including data result size, user interests, and available presentation budgets (e.g., screen real-estate), can impact the content determination simultaneously. Unfortunately, existing approaches do not have techniques for adequately handling these factors.
Accordingly, techniques are needed for providing improved data content determination in information-seeking systems.